On the Impact of Face Segmentation-Based Background Removal on Recognition and Morphing Attack Detection

TL;DR

This paper examines how face segmentation-based background removal affects face recognition and morphing attack detection in real-world scenarios, highlighting its impact on system performance and security.

Contribution

It provides a comprehensive analysis of various segmentation techniques and their influence on recognition and morphing attack detection across multiple models and datasets.

Findings

Segmentation impacts recognition accuracy and face image quality.

Segmentation systematically influences morphing attack detection performance.

Patterns link segmentation methods to performance variations.

Abstract

This study investigates the impact of face image background correction through segmentation on face recognition and morphing attack detection performance in realistic, unconstrained image capture scenarios. The motivation is driven by operational biometric systems such as the European Entry/Exit System (EES), which require facial enrolment at airports and other border crossing points where controlled backgrounds usually required for such captures cannot always be guaranteed, as well as by accessibility needs that may necessitate image capture outside traditional office environments. By analyzing how such preprocessing steps influence both recognition accuracy and security mechanisms, this work addresses a critical gap between usability-driven image normalization and the reliability requirements of large-scale biometric identification systems. Our study evaluates a comprehensive range of segmentation techniques, three families of morphing attack detection methods, and four distinct face recognition models, using databases that include both controlled and in-the-wild image captures. The results reveal consistent patterns linking segmentation to both recognition performance and face image quality. Additionally, segmentation is shown to systematically influence morphing attack detection performance. These findings highlight the need for careful consideration when deploying such preprocessing techniques in operational biometric systems.